{"pageNumber":"1225","pageRowStart":"30600","pageSize":"25","recordCount":184860,"records":[{"id":70182732,"text":"70182732 - 2015 - A rapid approach for automated comparison of independently derived stream networks","interactions":[],"lastModifiedDate":"2017-02-27T15:24:10","indexId":"70182732","displayToPublicDate":"2015-08-14T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1191,"text":"Cartography and Geographic Information Science","active":true,"publicationSubtype":{"id":10}},"title":"A rapid approach for automated comparison of independently derived stream networks","docAbstract":"

This paper presents an improved coefficient of line correspondence (CLC) metric for automatically assessing the similarity of two different sets of linear features. Elevation-derived channels at 1:24,000 scale (24K) are generated from a weighted flow-accumulation model and compared to 24K National Hydrography Dataset (NHD) flowlines. The CLC process conflates two vector datasets through a raster line-density differencing approach that is faster and more reliable than earlier methods. Methods are tested on 30 subbasins distributed across different terrain and climate conditions of the conterminous United States. CLC values for the 30 subbasins indicate 44–83% of the features match between the two datasets, with the majority of the mismatching features comprised of first-order features. Relatively lower CLC values result from subbasins with less than about 1.5 degrees of slope. The primary difference between the two datasets may be explained by different data capture criteria. First-order, headwater tributaries derived from the flow-accumulation model are captured more comprehensively through drainage area and terrain conditions, whereas capture of headwater features in the NHD is cartographically constrained by tributary length. The addition of missing headwaters to the NHD, as guided by the elevation-derived channels, can substantially improve the scientific value of the NHD.

","language":"English","publisher":"Taylor & Francis ","doi":"10.1080/15230406.2015.1060869","usgsCitation":"Stanislawski, L.V., Buttenfield, B.P., and Doumbouya, A.T., 2015, A rapid approach for automated comparison of independently derived stream networks: Cartography and Geographic Information Science, p. 435-448, https://doi.org/10.1080/15230406.2015.1060869.","productDescription":"14 p. ","startPage":"435","endPage":"448","ipdsId":"IP-066429","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":336303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-14","publicationStatus":"PW","scienceBaseUri":"58b548c2e4b01ccd54fddfc8","contributors":{"authors":[{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":673483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":184069,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":673485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doumbouya, Ariel T. atdoumbouya@usgs.gov","contributorId":5764,"corporation":false,"usgs":true,"family":"Doumbouya","given":"Ariel","email":"atdoumbouya@usgs.gov","middleInitial":"T.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":673484,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155988,"text":"70155988 - 2015 - Glaciers and ice caps outside Greenland","interactions":[],"lastModifiedDate":"2018-07-07T18:06:40","indexId":"70155988","displayToPublicDate":"2015-08-13T16:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Glaciers and ice caps outside Greenland","docAbstract":"

Mountain glaciers and ice caps cover an area of over 400 000 km2 in the Arctic, and are a major influence on global sea level (Gardner et al. 2011, 2013; Jacob et al. 2012). They gain mass by snow accumulation and lose mass by meltwater runoff. Where they terminate in water (ocean or lake), they also lose mass by iceberg calving. The climatic mass balance (Bclim, the difference between annual snow accumulation and annual meltwater runoff) is a widely used index of how glaciers respond to climate variability and change. The total mass balance (ΔM) is defined as the difference between annual snow accumulation and annual mass losses (by iceberg calving plus runoff).

","language":"English","publisher":"American Meteorological Society","publisherLocation":"Washington, D.C.","usgsCitation":"Sharp, M., Wolken, G., Burgess, D., Cogley, J., Copland, L., Thomson, L., Arendt, A., Wouters, B., Kohler, J., Andreassen, L.M., O’Neel, S., and Pelto, M., 2015, Glaciers and ice caps outside Greenland: Bulletin of the American Meteorological Society, v. 96, no. 7, p. S135-S137.","productDescription":"Sxvi., S267","startPage":"S135","endPage":"S137","numberOfPages":"288","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063693","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":306714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":306713,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www2.ametsoc.org/ams/index.cfm/publications/bulletin-of-the-american-meteorological-society-bams/state-of-the-climate/"}],"volume":"96","issue":"7","edition":"Supplement","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdb1abe4b08400b1fe13a5","contributors":{"authors":[{"text":"Sharp, Marin","contributorId":146359,"corporation":false,"usgs":false,"family":"Sharp","given":"Marin","email":"","affiliations":[{"id":12799,"text":"University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":567562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolken, G.","contributorId":146508,"corporation":false,"usgs":false,"family":"Wolken","given":"G.","email":"","affiliations":[],"preferred":false,"id":568070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, D.","contributorId":146509,"corporation":false,"usgs":false,"family":"Burgess","given":"D.","email":"","affiliations":[],"preferred":false,"id":568071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cogley, J.G.","contributorId":58549,"corporation":false,"usgs":true,"family":"Cogley","given":"J.G.","email":"","affiliations":[],"preferred":false,"id":568072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Copland, L.","contributorId":146510,"corporation":false,"usgs":false,"family":"Copland","given":"L.","affiliations":[],"preferred":false,"id":568073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomson, L.","contributorId":146511,"corporation":false,"usgs":false,"family":"Thomson","given":"L.","email":"","affiliations":[],"preferred":false,"id":568074,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arendt, A.","contributorId":146512,"corporation":false,"usgs":false,"family":"Arendt","given":"A.","email":"","affiliations":[],"preferred":false,"id":568075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wouters, B.","contributorId":146513,"corporation":false,"usgs":false,"family":"Wouters","given":"B.","email":"","affiliations":[],"preferred":false,"id":568076,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kohler, J.","contributorId":66476,"corporation":false,"usgs":true,"family":"Kohler","given":"J.","email":"","affiliations":[],"preferred":false,"id":568077,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Andreassen, L. M.","contributorId":146514,"corporation":false,"usgs":false,"family":"Andreassen","given":"L.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":568078,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"O’Neel, Shad 0000-0002-9185-0144 soneel@usgs.gov","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":166740,"corporation":false,"usgs":true,"family":"O’Neel","given":"Shad","email":"soneel@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":567561,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pelto, M.","contributorId":146515,"corporation":false,"usgs":false,"family":"Pelto","given":"M.","affiliations":[],"preferred":false,"id":568079,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70156021,"text":"70156021 - 2015 - Gene transcription in polar bears (Ursus maritimus) from disparate populations","interactions":[],"lastModifiedDate":"2015-08-13T14:42:28","indexId":"70156021","displayToPublicDate":"2015-08-13T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3093,"text":"Polar Biology","active":true,"publicationSubtype":{"id":10}},"title":"Gene transcription in polar bears (Ursus maritimus) from disparate populations","docAbstract":"

Polar bears in the Beaufort (SB) and Chukchi (CS) Seas experience different environments due primarily to a longer history of sea ice loss in the Beaufort Sea. Ecological differences have been identified as a possible reason for the generally poorer body condition and reproduction of Beaufort polar bears compared to those from the Chukchi, but the influence of exposure to other stressors remains unknown. We use molecular technology, quantitative PCR, to identify gene transcription differences among polar bears from the Beaufort and Chukchi Seas as well as captive healthy polar bears. We identified significant transcriptional differences among a priori groups (i.e., captive bears, SB 2012, SB 2013, CS 2013) for ten of the 14 genes of interest (i.e., CaM, HSP70, CCR3, TGFβ, COX2, THRα, T-bet, Gata3, CD69, and IL17); transcription levels of DRβ, IL1β, AHR, and Mx1 did not differ among groups. Multivariate analysis also demonstrated separation among the groups of polar bears. Specifically, we detected transcript profiles consistent with immune function impairment in polar bears from the Beaufort Sea, when compared with Chukchi and captive polar bears. Although there is no strong indication of differential exposure to contaminants or pathogens between CS and SB bears, there are clearly differences in important transcriptional responses between populations. Further investigation is warranted to refine interpretation of potential effects of described stress-related conditions for the SB population.

","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s00300-015-1705-0","usgsCitation":"Bowen, L., Miles, A.K., Waters-Dynes, S.C., Meyerson, R., Rode, K.D., and Atwood, T.C., 2015, Gene transcription in polar bears (Ursus maritimus) from disparate populations: Polar Biology, v. 38, no. 9, p. 1413-1427, https://doi.org/10.1007/s00300-015-1705-0.","productDescription":"15 p.","startPage":"1413","endPage":"1427","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2011-03-01","temporalEnd":"2012-12-31","ipdsId":"IP-064965","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":306680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-26","publicationStatus":"PW","scienceBaseUri":"55cdb1abe4b08400b1fe13a3","contributors":{"authors":[{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miles, A. Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waters-Dynes, Shannon C. 0000-0002-9707-4684 swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":567685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyerson, Randi","contributorId":146389,"corporation":false,"usgs":false,"family":"Meyerson","given":"Randi","email":"","affiliations":[{"id":16683,"text":"Toledo Zoo, Toledo, OH","active":true,"usgs":false}],"preferred":false,"id":567686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":567687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":567688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70156015,"text":"70156015 - 2015 - The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems","interactions":[],"lastModifiedDate":"2015-08-13T14:35:48","indexId":"70156015","displayToPublicDate":"2015-08-13T15:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems","docAbstract":"

Sediment has been shown to be a major stressor to coral reefs globally. Although many researchers have tested the impact of sedimentation on coral reef ecosystems in both the laboratory and the field and some have measured the impact of suspended sediment on the photosynthetic response of corals, there has yet to be a detailed investigation on how properties of the sediment itself can affect light availability for photosynthesis. We show that finer-grained and darker-colored sediment at higher suspended-sediment concentrations attenuates photosynthetically active radiation (PAR) significantly more than coarser, lighter-colored sediment at lower concentrations and provide PAR attenuation coefficients for various grain sizes, colors, and suspended-sediment concentrations that are needed for biophysical modeling. Because finer-grained sediment particles settle more slowly and are more susceptible to resuspension, they remain in the water column longer, thus causing greater net impact by reducing light essential for photosynthesis over a greater duration. This indicates that coral reef monitoring studies investigating sediment impacts should concentrate on measuring fine-grained lateritic and volcanic soils, as opposed to coarser-grained siliceous and carbonate sediment. Similarly, coastal restoration efforts and engineering solutions addressing long-term coral reef ecosystem health should focus on preferentially retaining those fine-grained soils rather than coarse silt and sand particles.

","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s00338-015-1268-0","usgsCitation":"Storlazzi, C.D., Norris, B., and Rosenberger, K.J., 2015, The influence of grain size, grain color, and suspended-sediment concentration on light attenuation: why fine-grained terrestrial sediment is bad for coral reef ecosystems: Coral Reefs, v. 34, no. 3, p. 967-975, https://doi.org/10.1007/s00338-015-1268-0.","productDescription":"9 p.","startPage":"967","endPage":"975","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060458","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":306678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-20","publicationStatus":"PW","scienceBaseUri":"55cdb1b1e4b08400b1fe13c7","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":567667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norris, Benjamin","contributorId":65001,"corporation":false,"usgs":true,"family":"Norris","given":"Benjamin","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":567668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":567669,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70156005,"text":"70156005 - 2015 - Sensitivity of intermittent streams to climate variations in the USA","interactions":[],"lastModifiedDate":"2016-06-15T16:01:05","indexId":"70156005","displayToPublicDate":"2015-08-13T15:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of intermittent streams to climate variations in the USA","docAbstract":"

There is a great deal of interest in the literature on streamflow changes caused by climate change because of the potential negative effects on aquatic biota and water supplies. Most previous studies have primarily focused on perennial streams, and there have been only a few studies examining the effect of climate variability on intermittent streams. Our objectives in this study were to (1) identify regions of similar zero-flow behavior, and (2) evaluate the sensitivity of intermittent streams to historical variability in climate in the United States. This study was carried out at 265 intermittent streams by evaluating: (1) correlations among time series of flow metrics (number of zero-flow events, the average of the central 50% and largest 10% of flows) with climate (magnitudes, durations and intensity), and (2) decadal changes in the seasonality and long-term trends of these flow metrics. Results identified five distinct seasonality patterns in the zero-flow events. In addition, strong associations between the low-flow metrics and historical changes in climate were found. The decadal analysis suggested no significant seasonal shifts or decade-to-decade trends in the low-flow metrics. The lack of trends or changes in seasonality is likely due to unchanged long-term patterns in precipitation over the time period examined.

","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Chichester, UK","doi":"10.1002/rra.2939","usgsCitation":"Eng, K., Wolock, D.M., and Dettinger, M., 2015, Sensitivity of intermittent streams to climate variations in the USA: River Research and Applications, v. 32, no. 5, p. 885-895, https://doi.org/10.1002/rra.2939.","productDescription":"11 p.","startPage":"885","endPage":"895","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066904","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":306676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.1064453125,\n 49.009050809382046\n ],\n [\n -104.0625,\n 41.0130657870063\n ],\n [\n -102.1728515625,\n 40.94671366508002\n ],\n [\n -102.0849609375,\n 36.98500309285596\n ],\n [\n -114.12597656249999,\n 37.020098201368114\n ],\n [\n -117.5537109375,\n 37.3002752813443\n ],\n [\n -120.14648437499999,\n 39.06184913429154\n ],\n [\n -119.92675781249999,\n 41.96765920367816\n ],\n [\n -124.27734374999999,\n 42.032974332441405\n ],\n [\n -124.45312499999999,\n 40.212440718286466\n ],\n [\n -121.6845703125,\n 36.13787471840729\n ],\n [\n -118.69628906249999,\n 34.08906131584996\n ],\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.82910156249999,\n 32.694865977875075\n ],\n [\n -111.09374999999999,\n 31.31610138349565\n ],\n [\n -108.19335937499999,\n 31.27855085894653\n ],\n [\n -108.19335937499999,\n 31.80289258670676\n ],\n [\n -106.34765625,\n 31.80289258670676\n ],\n [\n -104.80957031249999,\n 30.29701788337205\n ],\n [\n -104.32617187499999,\n 29.458731185355344\n ],\n [\n -103.095703125,\n 28.9600886880068\n ],\n [\n -102.39257812499999,\n 29.84064389983441\n ],\n [\n -101.77734374999999,\n 29.84064389983441\n ],\n [\n -100.634765625,\n 29.036960648558267\n ],\n [\n -99.49218749999999,\n 27.488781168937997\n ],\n [\n -99.1845703125,\n 26.352497858154\n ],\n [\n -97.119140625,\n 25.878994400196202\n ],\n [\n -97.42675781249999,\n 27.254629577800088\n ],\n [\n -96.328125,\n 28.613459424004414\n ],\n [\n -94.74609375,\n 29.305561325527698\n ],\n [\n -91.93359375,\n 29.57345707301757\n ],\n [\n -89.033203125,\n 28.998531814051795\n ],\n [\n -89.7802734375,\n 31.015278981711266\n ],\n [\n -91.3623046875,\n 30.93992433102347\n ],\n [\n -91.23046875,\n 32.99023555965106\n ],\n [\n -89.5166015625,\n 36.527294814546245\n ],\n [\n -83.60595703125,\n 36.63316209558658\n ],\n [\n -81.97998046875,\n 37.52715361723378\n ],\n [\n -82.6611328125,\n 38.22091976683121\n ],\n [\n -82.529296875,\n 38.444984668894705\n ],\n [\n -81.4306640625,\n 39.41922073655956\n ],\n [\n -80.85937499999999,\n 39.67337039176558\n ],\n [\n -80.5517578125,\n 40.212440718286466\n ],\n [\n -80.5078125,\n 41.86956082699455\n ],\n [\n -87.56103515625,\n 41.85319643776675\n ],\n [\n -87.8466796875,\n 42.52069952914966\n ],\n [\n -90.68115234375,\n 42.50450285299051\n ],\n [\n -91.23046875,\n 43.46886761482925\n ],\n [\n -96.17431640625,\n 43.48481212891603\n ],\n [\n -96.52587890625,\n 48.980216985374994\n ],\n [\n -104.1064453125,\n 49.009050809382046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-07","publicationStatus":"PW","scienceBaseUri":"55cdb1b0e4b08400b1fe13c1","contributors":{"authors":[{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":567628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":567629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dettinger, Mike 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":859,"corporation":false,"usgs":true,"family":"Dettinger","given":"Mike","email":"mddettin@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":567630,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155978,"text":"70155978 - 2015 - Survival, growth, and tag retention in age-0 Chinook Salmon implanted with 8-, 9-, and 12-mm PIT tags","interactions":[],"lastModifiedDate":"2015-08-13T14:09:42","indexId":"70155978","displayToPublicDate":"2015-08-13T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Survival, growth, and tag retention in age-0 Chinook Salmon implanted with 8-, 9-, and 12-mm PIT tags","docAbstract":"

The ability to represent a population of migratory juvenile fish with PIT tags becomes difficult when the minimum tagging size is larger than the average size at which fish begin to move downstream. Tags that are smaller (e.g., 8 and 9 mm) than the commonly used 12-mm PIT tags are currently available, but their effects on survival, growth, and tag retention in small salmonid juveniles have received little study. We evaluated growth, survival, and tag retention in age-0 Chinook Salmon Oncorhynchus tshawytscha of three size-groups: 40–49-mm fish were implanted with 8- and 9-mm tags, and 50– 59-mm and 60–69-mm fish were implanted with 8-, 9-, and 12-mm tags. Survival 28 d after tagging ranged from 97.8% to 100% across all trials, providing no strong evidence for a fish-size-related tagging effect or a tag size effect. No biologically significant effects of tagging on growth in FL (mm/d) or weight (g/d) were observed. Although FL growth in tagged fish was significantly reduced for the 40–49-mm and 50–59-mm groups over the first 7 d, growth rates were not different thereafter, and all fish were similar in size by the end of the trials (day 28). Tag retention across all tests ranged from 93% to 99%. We acknowledge that actual implantation of 8- or 9-mm tags into small fish in the field will pose additional challenges (e.g., capture and handling stress) beyond those observed in our laboratory. However, we conclude that experimental use of the smaller tags for small fish in the field is supported by our findings.

","language":"English","publisher":"American Fisheries Society","publisherLocation":"Lawrence, KS","doi":"10.1080/02755947.2015.1052163","usgsCitation":"Tiffan, K.F., Perry, R.W., Connor, W.P., Mullins, F.L., Rabe, C., and Nelson, D.D., 2015, Survival, growth, and tag retention in age-0 Chinook Salmon implanted with 8-, 9-, and 12-mm PIT tags: North American Journal of Fisheries Management, v. 35, no. 4, p. 845-852, https://doi.org/10.1080/02755947.2015.1052163.","productDescription":"8 p.","startPage":"845","endPage":"852","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059227","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":306673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-29","publicationStatus":"PW","scienceBaseUri":"55cdb1b0e4b08400b1fe13c3","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connor, William P.","contributorId":107589,"corporation":false,"usgs":false,"family":"Connor","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":567504,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullins, Frank L.","contributorId":146343,"corporation":false,"usgs":false,"family":"Mullins","given":"Frank","email":"","middleInitial":"L.","affiliations":[{"id":16677,"text":"U.S. Fish and Wildlife Service, Idaho Fishery Resource Office, 276 Dworshak Complex Drive, Orofino, ID 83544","active":true,"usgs":false}],"preferred":false,"id":567505,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rabe, Craig","contributorId":146341,"corporation":false,"usgs":false,"family":"Rabe","given":"Craig","email":"","affiliations":[{"id":16676,"text":"Nez Perce Tribe Department of Fisheries Resources Management, P.O. Box 365 Lapwai, ID 83540","active":true,"usgs":false}],"preferred":false,"id":567501,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, Doug D","contributorId":146342,"corporation":false,"usgs":false,"family":"Nelson","given":"Doug","email":"","middleInitial":"D","affiliations":[{"id":16676,"text":"Nez Perce Tribe Department of Fisheries Resources Management, P.O. Box 365 Lapwai, ID 83540","active":true,"usgs":false}],"preferred":false,"id":567502,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70155976,"text":"70155976 - 2015 - Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer-aquitard complexes","interactions":[],"lastModifiedDate":"2018-09-04T16:29:55","indexId":"70155976","displayToPublicDate":"2015-08-13T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2233,"text":"Journal of Contaminant Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer-aquitard complexes","docAbstract":"

This study evaluates the role of the Peclet number as affected by molecular diffusion in transient anomalous transport, which is one of the major knowledge gaps in anomalous transport, by combining Monte Carlo simulations and stochastic model analysis. Two alluvial settings containing either short- or long-connected hydrofacies are generated and used as media for flow and transport modeling. Numerical experiments show that 1) the Peclet number affects both the duration of the power-law segment of tracer breakthrough curves (BTCs) and the transition rate from anomalous to Fickian transport by determining the solute residence time for a given low-permeability layer, 2) mechanical dispersion has a limited contribution to the anomalous characteristics of late-time transport as compared to molecular diffusion due to an almost negligible velocity in floodplain deposits, and 3) the initial source dimensions only enhance the power-law tail of the BTCs at short travel distances. A tempered stable stochastic (TSS) model is then applied to analyze the modeled transport. Applications show that the time-nonlocal parameters in the TSS model relate to the Peclet number, Pe. In particular, the truncation parameter in the TSS model increases nonlinearly with a decrease in Pe due to the decrease of the mean residence time, and the capacity coefficient increases with an increase in molecular diffusion which is probably due to the increase in the number of immobile particles. The above numerical experiments and stochastic analysis therefore reveal that the Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer–aquitard complexes.

","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jconhyd.2015.04.001","usgsCitation":"Zhang, Y., Green, C., and Tick, G.R., 2015, Peclet number as affected by molecular diffusion controls transient anomalous transport in alluvial aquifer-aquitard complexes: Journal of Contaminant Hydrology, v. 177-178, p. 220-238, https://doi.org/10.1016/j.jconhyd.2015.04.001.","productDescription":"19 p.","startPage":"220","endPage":"238","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061229","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471875,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jconhyd.2015.04.001","text":"Publisher Index Page"},{"id":306666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"177-178","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdb1b0e4b08400b1fe13be","contributors":{"authors":[{"text":"Zhang, Yong","contributorId":19029,"corporation":false,"usgs":true,"family":"Zhang","given":"Yong","affiliations":[],"preferred":false,"id":567493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Christopher T. ctgreen@usgs.gov","contributorId":146339,"corporation":false,"usgs":true,"family":"Green","given":"Christopher T.","email":"ctgreen@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":567492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tick, Geoffrey R.","contributorId":146340,"corporation":false,"usgs":false,"family":"Tick","given":"Geoffrey","email":"","middleInitial":"R.","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":567494,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155956,"text":"70155956 - 2015 - Long-term shifts in the phenology of rare and endemic Rocky Mountain plants","interactions":[],"lastModifiedDate":"2015-08-25T13:22:47","indexId":"70155956","displayToPublicDate":"2015-08-13T14:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Long-term shifts in the phenology of rare and endemic Rocky Mountain plants","docAbstract":"

PREMISE OF THE STUDY: Mountainous regions support high plant productivity, diversity, and endemism, yet are highly vulnerable to climate change. Historical records and model predictions show increasing temperatures across high elevation regions including the Southern Rocky Mountains, which can have a strong influence on the performance and distribution of montane plant species. Rare plant species can be particularly vulnerable to climate change because of their limited abundance and distribution.

\n

METHODS: We tracked the phenology of rare and endemic species, which are identified as imperiled, across three different habitat types with herbarium records to determine if flowering time has changed over the last century, and if phenological change was related to shifts in climate.

\n

KEY RESULTS: We found that the flowering date of rare species has accelerated 3.1 d every decade (42 d total) since the late 1800s, with plants in sagebrush interbasins showing the strongest accelerations in phenology. High winter temperatures were associated with the acceleration of phenology in low elevation sagebrush and barren river habitats, whereas high spring temperatures explained accelerated phenology in the high elevation alpine habitat. In contrast, high spring temperatures delayed the phenology of plant species in the two low-elevation habitats and precipitation had mixed effects depending on the season.

\n

CONCLUSIONS: These results provide evidence for large shifts in the phenology of rare Rocky Mountain plants related to climate, which can have strong effects on plant fitness, the abundance of associated wildlife, and the future of plant conservation in mountainous regions.                   

","language":"English","publisher":"Botanical Society of America","publisherLocation":"Lawrence, KS","doi":"10.3732/ajb.1500156","usgsCitation":"Munson, S.M., and Sher, A.A., 2015, Long-term shifts in the phenology of rare and endemic Rocky Mountain plants: American Journal of Botany, v. 102, no. 8, p. 1268-1276, https://doi.org/10.3732/ajb.1500156.","productDescription":"9 p.","startPage":"1268","endPage":"1276","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065084","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3732/ajb.1500156","text":"Publisher Index Page"},{"id":306657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"102","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13a9","contributors":{"authors":[{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":567395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sher, Anna A","contributorId":146314,"corporation":false,"usgs":false,"family":"Sher","given":"Anna","email":"","middleInitial":"A","affiliations":[{"id":12651,"text":"University of Denver","active":true,"usgs":false}],"preferred":false,"id":567396,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155954,"text":"70155954 - 2015 - Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming","interactions":[],"lastModifiedDate":"2015-08-13T13:15:14","indexId":"70155954","displayToPublicDate":"2015-08-13T14:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming","docAbstract":"

Rejuvenation of previously intruded silicic magma is an important process leading to effusive rhyolite, which is the most common product of volcanism at calderas with protracted histories of eruption and unrest such as Yellowstone, Long Valley, and Valles, USA. Although orders of magnitude smaller in volume than rare caldera-forming super-eruptions, these relatively frequent effusions of rhyolite are comparable to the largest eruptions of the 20th century and pose a considerable volcanic hazard. However, the physical pathway from rejuvenation to eruption of silicic magma is unclear particularly because the time between reheating of a subvolcanic intrusion and eruption is poorly quantified. This study uses geospeedometry of trace element profiles with nanometer resolution in sanidine crystals to reveal that Yellowstone’s most recent volcanic cycle began when remobilization of a near- or sub-solidus silicic magma occurred less than 10 months prior to eruption, following a 220,000 year period of volcanic repose. Our results reveal a geologically rapid timescale for rejuvenation and effusion of ~3 km3 of high-silica rhyolite lava even after protracted cooling of the subvolcanic system, which is consistent with recent physical modeling that predict a timescale of several years or less. Future renewal of rhyolitic volcanism at Yellowstone is likely to require an energetic intrusion of mafic or silicic magma into the shallow subvolcanic reservoir and could rapidly generate an eruptible rhyolite on timescales similar to those documented here.

","language":"English","publisher":"Geological Society of America","publisherLocation":"Boulder, CO","doi":"10.1130/G36862.1","usgsCitation":"Till, C.B., Vazquez, J.A., and Boyce, J., 2015, Months between rejuvenation and volcanic eruption at Yellowstone caldera, Wyoming: Geology, v. 43, no. 8, p. 695-698, https://doi.org/10.1130/G36862.1.","productDescription":"4 p.","startPage":"695","endPage":"698","numberOfPages":"5","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063317","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":306656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone caldera","volume":"43","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-01","publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13ab","contributors":{"authors":[{"text":"Till, Christy B. cbtill@usgs.gov","contributorId":4394,"corporation":false,"usgs":true,"family":"Till","given":"Christy","email":"cbtill@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":true,"id":567389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":567388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyce, Jeremy W","contributorId":146313,"corporation":false,"usgs":false,"family":"Boyce","given":"Jeremy W","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":567390,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155863,"text":"70155863 - 2015 - Lilac and honeysuckle phenology data 1956–2014","interactions":[],"lastModifiedDate":"2015-09-16T10:25:32","indexId":"70155863","displayToPublicDate":"2015-08-13T12:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Lilac and honeysuckle phenology data 1956–2014","docAbstract":"

The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis ‘Red Rothomagensis’) and two cloned honeysuckle cultivars (Lonicera tatarica ‘Arnold Red’ and L. korolkowii ‘Zabeli’). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale. While minor changes in methods have occurred over time, and some documentation is lacking, outlier analyses identified fewer than 3% of records as unusually early or late. Lilac and honeysuckle phenology data have proven robust in both model development and climatic research.

","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London, UK","doi":"10.1038/sdata.2015.38","usgsCitation":"Rosemartin, A.H., Denny, E.G., Weltzin, J., Marsh, R.L., Wilson, B.E., Mehdipoor, H., Zurita-Milla, R., and Schwartz, M., 2015, Lilac and honeysuckle phenology data 1956–2014: Scientific Data, v. 2, 150038; 8 p., https://doi.org/10.1038/sdata.2015.38.","productDescription":"150038; 8 p.","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1956-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-061549","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":471877,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2015.38","text":"Publisher Index Page"},{"id":308175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-21","publicationStatus":"PW","scienceBaseUri":"55fa92c1e4b05d6c4e501aa1","contributors":{"authors":[{"text":"Rosemartin, Alyssa H.","contributorId":30910,"corporation":false,"usgs":true,"family":"Rosemartin","given":"Alyssa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":566620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denny, Ellen G.","contributorId":79803,"corporation":false,"usgs":true,"family":"Denny","given":"Ellen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":566621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weltzin, Jake F. jweltzin@usgs.gov","contributorId":296,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake F.","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":false,"id":566618,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marsh, R. Lee","contributorId":146211,"corporation":false,"usgs":false,"family":"Marsh","given":"R.","email":"","middleInitial":"Lee","affiliations":[{"id":16629,"text":"USA National Phenology Network, SNRE University of Arizona","active":true,"usgs":false}],"preferred":false,"id":566622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Bruce E.","contributorId":94944,"corporation":false,"usgs":true,"family":"Wilson","given":"Bruce","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":566623,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mehdipoor, Hamed","contributorId":146212,"corporation":false,"usgs":false,"family":"Mehdipoor","given":"Hamed","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":566624,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zurita-Milla, Raul","contributorId":146213,"corporation":false,"usgs":false,"family":"Zurita-Milla","given":"Raul","email":"","affiliations":[{"id":16630,"text":"Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":566625,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schwartz, Mark D.","contributorId":11092,"corporation":false,"usgs":true,"family":"Schwartz","given":"Mark D.","affiliations":[],"preferred":false,"id":566619,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70155814,"text":"70155814 - 2015 - Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","interactions":[],"lastModifiedDate":"2015-08-13T10:33:01","indexId":"70155814","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA)","docAbstract":"

Groundwater has provided 50–90 % of the total water supply in Antelope Valley, California (USA). The associated groundwater-level declines have led the Los Angeles County Superior Court of California to recently rule that the Antelope Valley groundwater basin is in overdraft, i.e., annual pumpage exceeds annual recharge. Natural recharge consists primarily of mountain-front recharge and is an important component of the total groundwater budget in Antelope Valley. Therefore, natural recharge plays a major role in the Court’s decision. The exact quantity and distribution of natural recharge is uncertain, with total estimates from previous studies ranging from 37 to 200 gigaliters per year (GL/year). In order to better understand the uncertainty associated with natural recharge and to provide a tool for groundwater management, a numerical model of groundwater flow and land subsidence was developed. The transient model was calibrated using PEST with water-level and subsidence data; prior information was incorporated through the use of Tikhonov regularization. The calibrated estimate of natural recharge was 36 GL/year, which is appreciably less than the value used by the court (74 GL/year). The effect of parameter uncertainty on the estimation of natural recharge was addressed using the Null-Space Monte Carlo method. A Pareto trade-off method was also used to portray the reasonableness of larger natural recharge rates. The reasonableness of the 74 GL/year value and the effect of uncertain pumpage rates were also evaluated. The uncertainty analyses indicate that the total natural recharge likely ranges between 34.5 and 54.3 GL/year.

","language":"English","publisher":"Springer","publisherLocation":"Heidelberg, Germany","doi":"10.1007/s10040-015-1281-y","usgsCitation":"Siade, A.J., Nishikawa, T., and Martin, P., 2015, Natural recharge estimation and uncertainty analysis of an adjudicated groundwater basin using a regional-scale flow and subsidence model (Antelope Valley, California, USA): Hydrogeology Journal, v. 23, no. 6, p. 1267-1291, https://doi.org/10.1007/s10040-015-1281-y.","productDescription":"25 p.","startPage":"1267","endPage":"1291","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037195","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":471880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-015-1281-y","text":"Publisher Index Page"},{"id":306633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.19503784179688,\n 35.16819542676796\n ],\n [\n -118.90228271484374,\n 34.84536693184099\n ],\n [\n -118.91189575195312,\n 34.78222760653013\n ],\n [\n -117.45620727539062,\n 34.30260622622907\n ],\n [\n -117.54959106445312,\n 35.163704834815874\n ],\n [\n -118.19503784179688,\n 35.16819542676796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-24","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13b1","contributors":{"authors":[{"text":"Siade, Adam J. asiade@usgs.gov","contributorId":1533,"corporation":false,"usgs":true,"family":"Siade","given":"Adam","email":"asiade@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nishikawa, Tracy 0000-0002-7348-3838 tnish@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-3838","contributorId":1515,"corporation":false,"usgs":true,"family":"Nishikawa","given":"Tracy","email":"tnish@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Peter pmmartin@usgs.gov","contributorId":799,"corporation":false,"usgs":true,"family":"Martin","given":"Peter","email":"pmmartin@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566454,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154775,"text":"70154775 - 2015 - Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","interactions":[],"lastModifiedDate":"2019-06-03T13:24:23","indexId":"70154775","displayToPublicDate":"2015-08-13T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales","docAbstract":"

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publicly available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0134781","usgsCitation":"Tack, J., and Fedy, B., 2015, Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales: PLoS ONE, v. 10, no. 8, e0134781: 18 p., https://doi.org/10.1371/journal.pone.0134781.","productDescription":"e0134781: 18 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066450","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0134781","text":"Publisher Index Page"},{"id":306635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"10","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-11","publicationStatus":"PW","scienceBaseUri":"55cdb1ace4b08400b1fe13a7","contributors":{"authors":[{"text":"Tack, Jason D. jtack@usgs.gov","contributorId":145460,"corporation":false,"usgs":true,"family":"Tack","given":"Jason D.","email":"jtack@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":564104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fedy, Bradley C.","contributorId":40536,"corporation":false,"usgs":true,"family":"Fedy","given":"Bradley C.","affiliations":[],"preferred":false,"id":564105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155827,"text":"70155827 - 2015 - Multiscale analysis of river networks using the R package linbin","interactions":[],"lastModifiedDate":"2017-11-22T17:41:33","indexId":"70155827","displayToPublicDate":"2015-08-13T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Multiscale analysis of river networks using the R package linbin","docAbstract":"

Analytical tools are needed in riverine science and management to bridge the gap between GIS and statistical packages that were not designed for the directional and dendritic structure of streams. We introduce linbin, an R package developed for the analysis of riverscapes at multiple scales. With this software, riverine data on aquatic habitat and species distribution can be scaled and plotted automatically with respect to their position in the stream network or—in the case of temporal data—their position in time. The linbin package aggregates data into bins of different sizes as specified by the user. We provide case studies illustrating the use of the software for (1) exploring patterns at different scales by aggregating variables at a range of bin sizes, (2) comparing repeat observations by aggregating surveys into bins of common coverage, and (3) tailoring analysis to data with custom bin designs. Furthermore, we demonstrate the utility of linbin for summarizing patterns throughout an entire stream network, and we analyze the diel and seasonal movements of tagged fish past a stationary receiver to illustrate how linbin can be used with temporal data. In short, linbin enables more rapid analysis of complex data sets by fisheries managers and stream ecologists and can reveal underlying spatial and temporal patterns of fish distribution and habitat throughout a riverscape.

","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2015.1044764","usgsCitation":"Welty, E.Z., Torgersen, C.E., Brenkman, S.J., Duda, J., and Armstrong, J., 2015, Multiscale analysis of river networks using the R package linbin: North American Journal of Fisheries Management, v. 4, no. 35, p. 802-809, https://doi.org/10.1080/02755947.2015.1044764.","productDescription":"8 p.","startPage":"802","endPage":"809","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061892","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":306629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"35","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-07-17","publicationStatus":"PW","scienceBaseUri":"55cdb1ade4b08400b1fe13af","contributors":{"authors":[{"text":"Welty, Ethan Z.","contributorId":146461,"corporation":false,"usgs":true,"family":"Welty","given":"Ethan","email":"","middleInitial":"Z.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":567958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":3578,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":566509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brenkman, Samuel J.","contributorId":138941,"corporation":false,"usgs":false,"family":"Brenkman","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":12587,"text":"Olympic National Park, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":567959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":3323,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey J.","email":"jduda@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":567960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Jonathan B.","contributorId":98567,"corporation":false,"usgs":true,"family":"Armstrong","given":"Jonathan B.","affiliations":[],"preferred":false,"id":567961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70148339,"text":"70148339 - 2015 - Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","interactions":[],"lastModifiedDate":"2020-10-29T20:20:51.220065","indexId":"70148339","displayToPublicDate":"2015-08-13T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1264,"text":"Cold Regions Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model","docAbstract":"

Glide snow avalanches are dangerous and difficult to predict. Despite substantial recent research there is still inadequate understanding regarding the controls of glide snow avalanche release. Glide snow avalanches often occur in similar terrain or the same locations annually, and repeat observations and prior work suggest that specific topography may be critical. Thus, to gain a better understanding of the terrain component of these types of avalanches we examined terrain parameters associated with the specific area of glide snow avalanche release in comparison to avalanche starting zones where no glide snow avalanches were observed (i.e. non-glide snow avalanche terrain).

Glide snow avalanche occurrences visible from the Going-to-the-Sun Road corridor in Glacier National Park, Montana from 2003 to 2013 are investigated using a database of all avalanche occurrences derived of daily observations each year from 1 April to 1 June. This yielded 192 glide snow avalanches in 53 distinct avalanche paths. Each avalanche was digitized in a GIS using satellite, oblique, and aerial imagery as reference. A set of 117 non-glide snow avalanche starting zones were also selected in this manner. These were start zones with avalanche activity potential, but without glide avalanches observed. Topographical parameters such as area, slope, aspect, curvature, potential incoming solar radiation, distance from ridge, and elevation were then derived for the entire dataset utilizing tools with a GIS and a 10 m DEM. Ground class and a glide factor were calculated using a four level classification index with in-situ observations and a land surface type layer in a GIS.

A total of 21 terrain variables were examined using a univariate analysis between areas where glide snow avalanches occurred and areas where glide snow avalanches were never observed, despite crack formation. Only two variables were not significantly different. The significantly different variables were then used to train a classification tree to distinguish between glide and non-glide snow avalanche terrain. A 10-fold cross validated tree resulted in four decision nodes to classify the data. The nodes split on glide factor, maximum slope angle, seasonal sum of incoming solar radiation, and maximum curvature to distinguish between glide snow avalanche and non-glide snow avalanche terrain with an unweighted average accuracy (RPC) of 0.95 and probability of detection of events (POD) of 0.99.

Finally, the results of the cross-validated tree were used in a GIS to examine other areas, not used in the training dataset of the classification tree, of potential glide snow avalanche release within Glacier National Park. Using this understanding of the role of topographic parameters on glide snow avalanche activity, a spatial terrain based model was developed to identify other areas with high glide snow avalanche potential outside of the immediate observation area. This simple spatial model correctly classified 78 percent of actual glide snow avalanche terrain (pixel count) of a small test area of four independent observed glide snow avalanches.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.coldregions.2015.08.002","usgsCitation":"Peitzsch, E.H., Hendrikx, J., and Fagre, D.B., 2015, Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model: Cold Regions Science and Technology, v. 120, p. 237-250, https://doi.org/10.1016/j.coldregions.2015.08.002.","productDescription":"14 p.","startPage":"237","endPage":"250","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061113","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":301089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.345703125,\n 48.23016176791893\n ],\n [\n -113.15093994140625,\n 48.23016176791893\n ],\n [\n -113.15093994140625,\n 48.980216985374994\n ],\n [\n -114.345703125,\n 48.980216985374994\n ],\n [\n -114.345703125,\n 48.23016176791893\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55780e29e4b032353cbeb6f1","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrikx, Jordy 0000-0001-6194-3596","orcid":"https://orcid.org/0000-0001-6194-3596","contributorId":140954,"corporation":false,"usgs":false,"family":"Hendrikx","given":"Jordy","email":"","affiliations":[{"id":13628,"text":"Department of Earth Sciences, P.O. Box 173480, Montana State University, Bozeman, MT, USA. 59717.","active":true,"usgs":false}],"preferred":false,"id":547718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461 dan_fagre@usgs.gov","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":2036,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel","email":"dan_fagre@usgs.gov","middleInitial":"B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":547719,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155830,"text":"sir20145167 - 2015 - Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma","interactions":[],"lastModifiedDate":"2018-02-05T15:03:57","indexId":"sir20145167","displayToPublicDate":"2015-08-13T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2014-5167","title":"Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma","docAbstract":"

A hydrogeological study including two numerical groundwater-flow models was completed for the Citizen Potawatomi Nation Tribal Jurisdictional Area of central Oklahoma. One numerical groundwater-flow model, the Citizen Potawatomi Nation model, encompassed the jurisdictional area and was based on the results of a regional-scale hydrogeological study and numerical groundwater flow model of the Central Oklahoma aquifer, which had a geographic extent that included the Citizen Potawatomi Nation Tribal Jurisdictional Area. The Citizen Potawatomi Nation numerical groundwater-flow model included alluvial aquifers not in the original model and improved calibration using automated parameter-estimation techniques. The Citizen Potawatomi Nation numerical groundwater-flow model was used to analyze the groundwater-flow system and the effects of drought on the volume of groundwater in storage and streamflow in the North Canadian River. A more detailed, local-scale inset model was constructed from the Citizen Potawatomi Nation model to estimate available groundwater resources for two Citizen Potawatomi Nation economic development zones near the North Canadian River, the geothermal supply area and the Iron Horse Industrial Park.

\n

Groundwater pumping rates at potential well locations were optimized using the most recent version of the U.S. Geological Survey Groundwater-Management Process for MODFLOW. The objectives of optimization were to determine if a total pumping rate of 500 gallons per minute could be pumped from 5 wells at the geothermal supply area and to maximize discharge from 16 wells at the Iron Horse Industrial Park without exceeding specified head drawdown constraints at the pumping wells and thus prevent groundwater depletion.

\n

The inset model was used to estimate North Canadian River streamflow depletion caused by optimized pumping at the Iron Horse Industrial Park because water quality was a concern, and the river may have degraded water quality compared to water in other parts of the alluvial aquifer. The fate of streamflow that infiltrates into groundwater because of pumping was not directly determined, but it was assumed that this water could end up in the well discharge, and was considered to be a maximum proportion of well discharge derived from the North Canadian River.

\n

The total optimized continuous pumping rate from five managed wells at the geothermal supply area was 638 gallons per minute, which exceeded the target pumping rate of 500 gallons per minute. The total continuous pumping rate from 16 wells at the Iron Horse Industrial Park was 1,472 gallons per minute, which induced stream infiltration of approximately 4.1 gallons per minute (approximately 0.3 percent of the total well discharge) from the North Canadian River.

\n

To estimate the effects of drought on water resources in the Citizen Potawatomi Nation Tribal Jurisdictional Area, a hypothetical 10-year drought during which precipitation would decrease by 50 percent was simulated by decreasing model groundwater recharge by the same proportion for the period 1990–2000 of the transient model. The effects of the drought were estimated by calculating the change in the volume of groundwater storage and groundwater flow to streams at the end of the drought period, and the change in simulated streamflow in the North Canadian River at the streamflow-gaging station at Shawnee, Okla., during and after the drought.

\n

The hypothetical decrease in recharge during the simulated drought caused groundwater in storage over the entire model in the study area to decrease by 361,500 acre-feet (14,100 acre-feet in the North Canadian River alluvial aquifer and 347,400 acre-feet in the Central Oklahoma aquifer), or approximately 0.2 percent of the total groundwater in storage over the drought period. This small percentage of groundwater loss showed that the Central Oklahoma aquifer as a bedrock aquifer has relatively low rates of recharge from the surface relative to the approximate storage. The budget for base flow to the North Canadian River indicated that the change in groundwater flow to the North Canadian River decreased during the 10-year drought by 386,500 acre-feet, or 37 percent. In all other parts of the Citizen Potawatomi Nation Tribal Jurisdictional Area, base flow decreased by 292,000 acre-feet, or 28 percent. Streamflow in the North Canadian River at the streamflow-gaging station at Shawnee, Okla., decreased during the hypothetical drought by as much as 28 percent, and the mean change in streamflow decreased as much as 16 percent. Streamflow at the Shawnee streamflow-gaging station did not recover to nondrought conditions until about 3 years after the simulated drought ended, during the relatively wet year of 2007.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145167","collaboration":"Prepared in cooperation with the Citizen Potawatomi Nation","usgsCitation":"Ryter, D.R., Kunkel, C.D., Peterson, S.M., and Traylor, J.P., 2018, Numerical simulation of groundwater flow, resource optimization, and potential effects of prolonged drought for the Citizen Potawatomi Nation Tribal Jurisdictional Area, central Oklahoma (ver. 1.2, February 2018), U.S. Geological Survey Scientific Investigations Report 2014–5167, 27 p., https://doi.org/10.3133/sir20145167.","productDescription":"viii, 27 p.","numberOfPages":"39","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058249","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":306619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2014/5167/coverthb4.jpg"},{"id":350836,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5167/sir20145167.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2014–5167"},{"id":350837,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2014/5167/versionHist_v1_2.txt","text":"Version History","size":"1.21 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2014–5167 Version History"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Citizen Potawatomi National Tribal Jurisdictional Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.30453491210938,\n 35.5802672918083\n ],\n [\n -97.28805541992188,\n 34.96137177524017\n ],\n [\n -97.25784301757812,\n 34.951241964789645\n ],\n [\n -97.17544555664062,\n 34.928726792983845\n ],\n [\n -97.08892822265625,\n 34.929852698375804\n ],\n [\n -97.00790405273438,\n 34.90620544067929\n ],\n [\n -96.93511962890624,\n 34.96812428662085\n ],\n [\n -96.86920166015625,\n 34.92760087214065\n ],\n [\n -96.82525634765625,\n 34.94448806230625\n ],\n [\n -96.79641723632812,\n 34.939985151560435\n ],\n [\n -96.79229736328125,\n 34.8971951696173\n ],\n [\n -96.75796508789062,\n 34.90057413710918\n ],\n [\n -96.734619140625,\n 34.86565143737161\n ],\n [\n -96.71539306640625,\n 34.858890491257824\n ],\n [\n -96.734619140625,\n 35.41479572901859\n ],\n [\n -96.7510986328125,\n 35.40360292969232\n ],\n [\n -96.78543090820312,\n 35.40696093270201\n ],\n [\n -96.8280029296875,\n 35.42374884923695\n ],\n [\n -96.866455078125,\n 35.431582013221295\n ],\n [\n -96.87332153320311,\n 35.39912537474419\n ],\n [\n -96.84997558593749,\n 35.37337460834958\n ],\n [\n -96.82662963867186,\n 35.36105611877928\n ],\n [\n -96.82937622070312,\n 35.345375322554474\n ],\n [\n -96.86508178710936,\n 35.35321610123821\n ],\n [\n -96.87744140625,\n 35.33641350071231\n ],\n [\n -96.8994140625,\n 35.31736632923788\n ],\n [\n -96.9598388671875,\n 35.33641350071231\n ],\n [\n -97.00790405273438,\n 35.36105611877928\n ],\n [\n -97.04086303710938,\n 35.394647571120544\n ],\n [\n -97.04635620117188,\n 35.428225036228106\n ],\n [\n -97.10678100585936,\n 35.460669951495305\n ],\n [\n -97.15072631835938,\n 35.47856499535729\n ],\n [\n -97.15896606445312,\n 35.49757411565533\n ],\n [\n -97.16720581054688,\n 35.4925427272974\n ],\n [\n -97.18231201171875,\n 35.50372316239306\n ],\n [\n -97.20634460449219,\n 35.50316417759601\n ],\n [\n -97.23381042480469,\n 35.51434313431818\n ],\n [\n -97.2454833984375,\n 35.53054985697859\n ],\n [\n -97.24891662597656,\n 35.55178129792757\n ],\n [\n -97.28118896484375,\n 35.58808520476325\n ],\n [\n -97.30316162109375,\n 35.595902354416566\n ],\n [\n -97.30453491210938,\n 35.5802672918083\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: February 24, 2016; Version 1.2: February 5, 2018","contact":"

Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th, Bldg 7
Oklahoma City, OK 73116
http://ok.water.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2015-08-13","revisedDate":"2018-02-05","noUsgsAuthors":false,"publicationDate":"2015-08-13","publicationStatus":"PW","scienceBaseUri":"56cee273e4b015c306ec5eee","contributors":{"authors":[{"text":"Ryter, Derek W. 0000-0002-2488-626X dryter@usgs.gov","orcid":"https://orcid.org/0000-0002-2488-626X","contributorId":3395,"corporation":false,"usgs":true,"family":"Ryter","given":"Derek","email":"dryter@usgs.gov","middleInitial":"W.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kunkel, Christopher D. ckunkel@usgs.gov","contributorId":5717,"corporation":false,"usgs":true,"family":"Kunkel","given":"Christopher","email":"ckunkel@usgs.gov","middleInitial":"D.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":false,"id":566519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Steven M. 0000-0002-9130-1284 speterson@usgs.gov","orcid":"https://orcid.org/0000-0002-9130-1284","contributorId":847,"corporation":false,"usgs":true,"family":"Peterson","given":"Steven","email":"speterson@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Traylor, Jonathan P. 0000-0002-2008-1923 jtraylor@usgs.gov","orcid":"https://orcid.org/0000-0002-2008-1923","contributorId":5322,"corporation":false,"usgs":true,"family":"Traylor","given":"Jonathan","email":"jtraylor@usgs.gov","middleInitial":"P.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566521,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154747,"text":"70154747 - 2015 - Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin","interactions":[],"lastModifiedDate":"2016-04-13T12:14:03","indexId":"70154747","displayToPublicDate":"2015-08-13T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Mortality patterns and detection bias from carcass data: An example from wolf recovery in Wisconsin","docAbstract":"

We developed models and provide computer code to make carcass recovery data more useful to wildlife managers. With these tools, wildlife managers can understand the spatial, temporal (e.g., across time periods, seasons), and demographic patterns in mortality causes from carcass recovery datasets. From datasets of radio-collared and non-collared carcasses, managers can calculate the detection bias by mortality cause in a non-collared carcass dataset compared to a collared carcass dataset. As a first step, we provide a standard procedure to assign mortality causes to carcasses. We provide an example of these methods for radio-collared wolves (n = 208) and non-collared wolves (n = 668) found dead in Wisconsin (1979–2012). We analyzed differences in mortality cause relative to season, age and sex classes, wolf harvest zones, and recovery phase (1979–1995: initial recovery, 1996–2002: early growth, 2003–2012: late growth). Seasonally, illegal kills and natural deaths were proportionally higher in winter (Oct–Mar) than summer (Apr–Sep) for collared wolves, whereas vehicle strikes and legal kills were higher in summer than winter. Spatially, more illegally killed collared wolves occurred in eastern wolf harvest zones where wolves reestablished more slowly and in the central forest region where optimal habitat is isolated by agriculture. Natural mortalities of collared wolves (e.g., disease, intraspecific strife, or starvation) were highest in western wolf harvest zones where wolves established earlier and existed at higher densities. Calculating detection bias in the non-collared dataset revealed that more than half of the non-collared carcasses on the landscape are not found. The lowest detection probabilities for non-collared carcasses (0.113–0.176) occurred in winter for natural, illegal, and unknown mortality causes.

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Climate change predictions include warming and drying trends, which are expected to be particularly pronounced in the southwestern United States. In this region, grassland dynamics are tightly linked to available moisture, yet it has proven difficult to resolve what aspects of climate drive vegetation change. In part, this is because it is unclear how heterogeneity in soils affects plant responses to climate. Here, we combine climate and soil properties with a mechanistic soil water model to explain temporal fluctuations in perennial grass cover, quantify where and the degree to which incorporating soil water dynamics enhances our ability to understand temporal patterns, and explore the potential consequences of climate change by assessing future trajectories of important climate and soil water variables. Our analyses focused on long-term (20–56 years) perennial grass dynamics across the Colorado Plateau, Sonoran, and Chihuahuan Desert regions. Our results suggest that climate variability has negative effects on grass cover, and that precipitation subsidies that extend growing seasons are beneficial. Soil water metrics, including the number of dry days and availability of water from deeper (>30 cm) soil layers, explained additional grass cover variability. While individual climate variables were ranked as more important in explaining grass cover, collectively soil water accounted for 40–60% of the total explained variance. Soil water conditions were more useful for understanding the responses of C3 than C4 grass species. Projections of water balance variables under climate change indicate that conditions that currently support perennial grasses will be less common in the future, and these altered conditions will be more pronounced in the Chihuahuan Desert and Colorado Plateau. We conclude that incorporating multiple aspects of climate and accounting for soil variability can improve our ability to understand patterns, identify areas of vulnerability, and predict the future of desert grasslands.

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lower Snake River reservoir","docAbstract":"

Subyearling fall Chinook salmon (Oncorhynchus tshawytscha) in the Columbia River basin exhibit a transient rearing strategy and depend on connected shoreline habitats during freshwater rearing. Impoundment has greatly reduced the amount of shallow-water rearing habitat that is exacerbated by the steep topography of reservoirs. Periodic dredging creates opportunities to strategically place spoils to increase the amount of shallow-water habitat for subyearlings while at the same time reducing the amount of unsuitable area that is often preferred by predators. We assessed the amount and spatial arrangement of subyearling rearing habitat in Lower Granite Reservoir on the Snake River to guide future habitat improvement efforts. A spatially explicit habitat assessment was conducted using physical habitat data, two-dimensional hydrodynamic modelling and a statistical habitat model in a geographic information system framework. We used field collections of subyearlings and a common predator [smallmouth bass (Micropterus dolomieu)] to draw inferences about predation risk within specific habitat types. Most of the high-probability rearing habitat was located in the upper half of the reservoir where gently sloping landforms created low lateral bed slopes and shallow-water habitats. Only 29% of shorelines were predicted to be suitable (probability >0.5) for subyearlings, and the occurrence of these shorelines decreased in a downstream direction. The remaining, less suitable areas were composed of low-probability habitats in unmodified (25%) and riprapped shorelines (46%). As expected, most subyearlings were found in high-probability habitat, while most smallmouth bass were found in low-probability locations. However, some subyearlings were found in low-probability habitats, such as riprap, where predation risk could be high. Given their transient rearing strategy and dependence on shoreline habitats, subyearlings could benefit from habitat creation efforts in the lower reservoir where high-probability habitat is generally lacking. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.2934","usgsCitation":"Tiffan, K.F., Hatten, J.R., and Trachtenbarg, D.A., 2015, Assessing juvenile salmon rearing habitat and associated predation risk in a lower Snake River reservoir: River Research and Applications, v. 32, no. 5, p. 1030-1038, https://doi.org/10.1002/rra.2934.","productDescription":"9 p.","startPage":"1030","endPage":"1038","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064916","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":306672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Snake river reservior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.69653320312499,\n 46.67582559793001\n ],\n [\n -117.53173828125,\n 46.66074749832068\n ],\n [\n -117.40264892578124,\n 46.6268063953552\n ],\n [\n -117.29553222656249,\n 46.53052428878426\n ],\n [\n -117.21313476562499,\n 46.411351502899215\n ],\n [\n -117.07305908203124,\n 46.39998810407942\n ],\n [\n -117.07855224609376,\n 46.34313560260196\n ],\n [\n -117.0098876953125,\n 46.28622391806706\n ],\n [\n -116.98242187499999,\n 46.214050815339526\n ],\n [\n -116.97418212890625,\n 46.11513371326539\n ],\n [\n -116.91375732421875,\n 46.128459837044915\n ],\n [\n -116.93847656250001,\n 46.26534147068603\n ],\n [\n -116.99615478515624,\n 46.36588370484979\n ],\n [\n -116.98242187499999,\n 46.40188216826328\n ],\n [\n -116.82861328125001,\n 46.430285240839964\n ],\n [\n -116.84234619140624,\n 46.464349400461124\n ],\n [\n -117.103271484375,\n 46.45110475854117\n ],\n [\n -117.26257324218749,\n 46.56452573114373\n ],\n [\n -117.3065185546875,\n 46.62492015414768\n ],\n [\n -117.3944091796875,\n 46.685247274319565\n ],\n [\n -117.46307373046874,\n 46.717268685073954\n ],\n [\n -117.69653320312499,\n 46.72291755083757\n ],\n [\n -117.69653320312499,\n 46.67582559793001\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-03","publicationStatus":"PW","scienceBaseUri":"55cdb1a7e4b08400b1fe139d","contributors":{"authors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":567530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trachtenbarg, David A","contributorId":146351,"corporation":false,"usgs":false,"family":"Trachtenbarg","given":"David","email":"","middleInitial":"A","affiliations":[{"id":16680,"text":"U.S. Army Corps of Engineers, Walla Walla District, Walla Walla, WA 99362","active":true,"usgs":false}],"preferred":false,"id":567531,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70154780,"text":"70154780 - 2015 - Detecting mismatches of bird migration stopover and tree phenology in response to changing climate","interactions":[],"lastModifiedDate":"2018-09-04T15:33:56","indexId":"70154780","displayToPublicDate":"2015-08-13T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Detecting mismatches of bird migration stopover and tree phenology in response to changing climate","docAbstract":"

Migratory birds exploit seasonal variation in resources across latitudes, timing migration to coincide with the phenology of food at stopover sites. Differential responses to climate in phenology across trophic levels can result in phenological mismatch; however, detecting mismatch is sensitive to methodology. We examined patterns of migrant abundance and tree flowering, phenological mismatch, and the influence of climate during spring migration from 2009 to 2011 across five habitat types of the Madrean Sky Islands in southeastern Arizona, USA. We used two metrics to assess phenological mismatch: synchrony and overlap. We also examined whether phenological overlap declined with increasing difference in mean event date of phenophases. Migrant abundance and tree flowering generally increased with minimum spring temperature but depended on annual climate by habitat interactions. Migrant abundance was lowest and flowering was highest under cold, snowy conditions in high elevation montane conifer habitat while bird abundance was greatest and flowering was lowest in low elevation riparian habitat under the driest conditions. Phenological synchrony and overlap were unique and complementary metrics and should both be used when assessing mismatch. Overlap declined due to asynchronous phenologies but also due to reduced migrant abundance or flowering when synchrony was actually maintained. Overlap declined with increasing difference in event date and this trend was strongest in riparian areas. Montane habitat specialists may be at greatest risk of mismatch while riparian habitat could provide refugia during dry years for phenotypically plastic species. Interannual climate patterns that we observed match climate change projections for the arid southwest, altering stopover habitat condition.

","language":"English","publisher":"Springer","doi":"10.1007/s00442-015-3293-7","usgsCitation":"Kellermann, J.L., and van Riper, C., 2015, Detecting mismatches of bird migration stopover and tree phenology in response to changing climate: Oecologia, v. 178, no. 4, p. 1227-1238, https://doi.org/10.1007/s00442-015-3293-7.","productDescription":"12 p.","startPage":"1227","endPage":"1238","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-043585","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":306636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Madrean Sky Island Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.741943359375,\n 31.3348710339506\n ],\n [\n -111.741943359375,\n 32.84267363195431\n ],\n [\n -109.4512939453125,\n 32.84267363195431\n ],\n [\n -109.4512939453125,\n 31.3348710339506\n ],\n [\n -111.741943359375,\n 31.3348710339506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"178","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-31","publicationStatus":"PW","scienceBaseUri":"55cdb1aae4b08400b1fe13a1","contributors":{"authors":[{"text":"Kellermann, Jherime L.","contributorId":139843,"corporation":false,"usgs":false,"family":"Kellermann","given":"Jherime","email":"","middleInitial":"L.","affiliations":[{"id":13292,"text":"Ecology & Evolutionary Biology ,University of Arizona","active":true,"usgs":false}],"preferred":false,"id":564124,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":564123,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70155509,"text":"sir20155106 - 2015 - Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","interactions":[],"lastModifiedDate":"2015-10-26T14:28:11","indexId":"sir20155106","displayToPublicDate":"2015-08-12T15:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5106","title":"Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus physiographic province","docAbstract":"

In response to challenges to groundwater availability posed by historic land-use practices, expanding development of hydrocarbon resources, and drought, the U.S. Geological Survey Groundwater Resources Program began a regional assessment of the Appalachian Plateaus aquifers in 2013 that incorporated a hydrologic landscape approach to estimate all components of the hydrologic system: surface runoff, base flow from groundwater, and interaction with atmospheric water (precipitation and evapotranspiration). This assessment was intended to complement other Federal and State investigations and provide foundational groundwater-related datasets in the Appalachian Plateaus.

\n

A regional Soil-Water-Balance model was constructed for a 160,000-square-mile study area that extended to the topographic divide of all streams originating outside but flowing into areas underlain by Appalachian Plateaus aquifers. The model incorporated soil, landscape, and climate variables to estimate an annual water budget for the 32-year period from 1980 to 2011 and was calibrated using base-flow data estimated by hydrograph separation techniques from 20 streamflow gaging stations across the study area. Over this period, an average of 47 inches per year (in/yr) of precipitation fell on Appalachian Plateaus aquifers. Simulations from the regional Soil-Water-Balance model indicate that only 19 percent of the precipitation or an average 9 in/yr recharged aquifers, and 19 percent resulted in surface runoff to streams. The remaining 62 percent, an average of 27 in/yr of water, was returned to the atmosphere via evapotranspiration. Because withdrawals from aquifers due to pumping equated to less than 1 percent of the water budget, differences in predevelopment and postdevelopment regional water budgets of the Appalachian Plateaus were minimal. Storage changes caused by filling of abandoned coal-mine aquifers and long-term differences in aquifer storage resulting from climate fluctuations constitute a small portion of the overall water budget.

\n

The percentage of precipitation that results in recharge, runoff, or evapotranspiration from the landscape varies annually by up to a factor of two depending on temporal changes in prevailing climate conditions and spatial changes in basin characteristics, precipitation patterns, and sources of atmospheric moisture over a large study area. A comparison of water-budget estimates from the regional Soil-Water-Balance model for a dry year (1988) and wet year (2004) showed that evapotranspiration accounts for most of the annual differences in precipitation. As a portion of annual precipitation, evapotranspiration ranged from 69 percent (dry year) to 52 percent (wet year), a range four times greater than the 15 percent (dry year) to 18 percent (wet year) range estimated for recharge. Evapotranspiration as a percentage of precipitation peaks during dry periods, whereas base flow and runoff tend to reach minimum values. During wet periods, this relationship is reversed and base flow and runoff as a percentage of precipitation generally peak while evapotranspiration percentages reach minimum values. Annual recharge in the Appalachian Plateaus reaches a maximum at near 20 percent of annual precipitation, regardless of the severity of wet conditions.

\n

Hydrograph separation data from 849 streamflow gaging stations in the study area were used to assess trends in streamflow, base flow, surface runoff, and base-flow index, or ratio of base flow to streamflow, in the Appalachian Plateaus for the period from 1930 to 2011. Annual data anomalies for each of the four variables were individually defined as the annual standard deviation from the mean at all 849 streamflow gaging stations. Annual data anomalies confirm the close relation of annual precipitation to both base flow and runoff components of streamflow, and both components increased during the period of analysis. Around 1970, conditions shifted streamflow from values generally below to above long-term means. At a regional scale, increases in base flow account for most of these observed increases in mean annual streamflow. The independence of the base-flow index to annual climate trends indicate that changes in the components of streamflow of the Appalachian Plateaus are probably in response to shifts in seasonal precipitation or widespread land-use practices.

\n

A subset of 77 index streamgages, defined as having 60 or more years of complete record between the years 1930 and 2011 with no more than 20 percent missing data, was selected to show spatial patterns of change in the water budget. Data from the index streamgages showed that the overall trends in base flow are dependent upon the period of evaluation. Long-term (1930–2011) increases in base flow were observed throughout the study area. For two shorter periods (1930–1969 and 1970–2011) trends in base flow were largely negative. In general, spatial patterns of change in streamflow, base flow, and runoff were mixed but generally consistent with prevailing climate patterns and land-use changes.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155106","collaboration":"Groundwater Resources Program","usgsCitation":"McCoy, K.J., Yager, R.M., Nelms, D.L., Ladd, D.E., Monti, Jack, Jr., and Kozar, M.D., 2015, Hydrologic budget and conditions of Permian, Pennsylvanian, and Mississippian aquifers in the Appalachian Plateaus Physiographic Province (ver. 1.1, October 2015): U.S. Geological Survey Scientific Investigations Report 2015–5106, 77 p., https://dx.doi.org/10.3133/sir20155106.","productDescription":"vii, 77 p.","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060623","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":306582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5106/sir20155106.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5106"},{"id":306581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5106/images/coverthb.jpg"},{"id":309929,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2015/5106/versionHist.txt","text":"October 26, 2015","size":"1.06 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2015-5106"}],"country":"United States","state":"Alabama, Kentucky, Maryland, Ohio, Pennslyvania, Virginia, Tennessee, West Virginia","otherGeospatial":"Mississippian aquifer, Pennsylvanian aquifer, Permian aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.31103515625,\n 41.705728515237524\n ],\n [\n -81.2109375,\n 41.83682786072714\n ],\n [\n -82.79296874999999,\n 41.36031866306708\n ],\n [\n -83.8037109375,\n 38.66835610151509\n ],\n [\n -86.98974609375,\n 34.97600151317591\n ],\n [\n -88.22021484375,\n 34.79576153473033\n ],\n [\n -88.39599609375,\n 32.62087018318113\n ],\n [\n -85.4736328125,\n 34.95799531086792\n ],\n [\n -83.3203125,\n 36.5978891330702\n ],\n [\n -80.22216796875,\n 37.474858084971046\n ],\n [\n -78.5302734375,\n 39.707186656826565\n ],\n [\n -76.31103515625,\n 41.705728515237524\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 13, 2015; Version 1.1: October 26, 2015","contact":"

Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
http://va.water.usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"publishedDate":"2015-08-13","revisedDate":"2015-10-26","noUsgsAuthors":false,"publicationDate":"2015-08-13","publicationStatus":"PW","scienceBaseUri":"562f4eb5e4b093cee780a293","contributors":{"authors":[{"text":"McCoy, Kurt J. 0000-0002-9756-8238 kjmccoy@usgs.gov","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":1391,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt","email":"kjmccoy@usgs.gov","middleInitial":"J.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":565613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yager, Richard M. 0000-0001-7725-1148 ryager@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-1148","contributorId":950,"corporation":false,"usgs":true,"family":"Yager","given":"Richard","email":"ryager@usgs.gov","middleInitial":"M.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelms, David L. 0000-0001-5747-642X dlnelms@usgs.gov","orcid":"https://orcid.org/0000-0001-5747-642X","contributorId":1892,"corporation":false,"usgs":true,"family":"Nelms","given":"David","email":"dlnelms@usgs.gov","middleInitial":"L.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ladd, David E. 0000-0002-9247-7839 deladd@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":1646,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"deladd@usgs.gov","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monti,, Jack Jr. jmonti@usgs.gov","contributorId":145900,"corporation":false,"usgs":true,"family":"Monti,","given":"Jack","suffix":"Jr.","email":"jmonti@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":565617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kozar, Mark D. 0000-0001-7755-7657 mdkozar@usgs.gov","orcid":"https://orcid.org/0000-0001-7755-7657","contributorId":1963,"corporation":false,"usgs":true,"family":"Kozar","given":"Mark","email":"mdkozar@usgs.gov","middleInitial":"D.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":565618,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70148627,"text":"70148627 - 2015 - Strong ground motion inferred from liquefaction caused by the 1811-1812 New Madrid, Missouri, earthquakes","interactions":[],"lastModifiedDate":"2015-10-05T15:38:07","indexId":"70148627","displayToPublicDate":"2015-08-12T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Strong ground motion inferred from liquefaction caused by the 1811-1812 New Madrid, Missouri, earthquakes","docAbstract":"

Peak ground accelerations (PGAs) in the epicentral region of the 1811–1812 New Madrid, Missouri, earthquakes are inferred from liquefaction to have been no greater than ∼0.35g. PGA is inferred in an 11,380  km2 area in the Lower Mississippi Valley in Arkansas and Missouri where liquefaction was extensive in 1811–1812. PGA was inferred by applying liquefaction probability curves, which were originally developed for liquefaction hazard mapping, to detailed maps of liquefaction by Obermeier (1989). The low PGA is inferred because both a shallow (1.5 m deep) water table and a large moment magnitude (M 7.7) earthquake were assumed in the analysis. If a deep (5.0 m) water table and a small magnitude (M 6.8) earthquake are assumed, the maximum inferred PGA is 1.10g. Both inferred PGA values are based on an assumed and poorly constrained correction for sand aging. If an aging correction is not assumed, then the inferred PGA is no greater than 0.22g. A low PGA value may be explained by nonlinear site response. Soils in the study area have an averageVS30 of 220±15  m/s. A low inferred PGA is consistent with PGA values estimated from ground‐motion prediction equations that have been proposed for the New Madrid seismic zone when these estimates are corrected for nonlinear soil site effects. This application of liquefaction probability curves demonstrates their potential usefulness in paleoseismology.

","language":"English","publisher":"Seismological Society of Amercia","doi":"10.1785/0120130258","usgsCitation":"Holzer, T.L., Noce, T.E., and Bennett, M.J., 2015, Strong ground motion inferred from liquefaction caused by the 1811-1812 New Madrid, Missouri, earthquakes: Bulletin of the Seismological Society of America, v. 105, no. 5, p. 2589-2603, https://doi.org/10.1785/0120130258.","productDescription":"15 p.","startPage":"2589","endPage":"2603","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045331","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":306610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"New Madrid","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07989501953125,\n 36.93013456125331\n ],\n [\n -89.1815185546875,\n 36.9367208722872\n ],\n [\n -89.3682861328125,\n 36.89939091854292\n ],\n [\n -89.6484375,\n 36.87742358748459\n ],\n [\n -89.72808837890625,\n 36.79169061907076\n ],\n [\n -89.76654052734375,\n 36.39696752441779\n ],\n [\n -90.2801513671875,\n 36.39033486213652\n ],\n [\n -90.384521484375,\n 36.24870331653198\n ],\n [\n -90.47241210937499,\n 36.113471382052175\n ],\n [\n -90.4888916015625,\n 35.940212068887455\n ],\n [\n -90.50811767578125,\n 35.753199435570316\n ],\n [\n -90.50811767578125,\n 35.63720889099896\n ],\n [\n -90.49163818359375,\n 35.55010533588552\n ],\n [\n -90.31585693359375,\n 35.52104976129943\n ],\n [\n -89.88739013671874,\n 35.543401137387356\n ],\n [\n -89.813232421875,\n 35.54116627999815\n ],\n [\n -89.65118408203125,\n 35.808904044068626\n ],\n [\n -89.571533203125,\n 35.99800750540412\n ],\n [\n -89.50836181640625,\n 35.99578538642032\n ],\n [\n -89.4781494140625,\n 36.055760619006776\n ],\n [\n -89.55780029296875,\n 36.0824016199585\n ],\n [\n -89.46990966796875,\n 36.17779108329074\n ],\n [\n -89.23919677734375,\n 36.43896124085945\n ],\n [\n -89.09088134765625,\n 36.74548692469868\n ],\n [\n -89.07989501953125,\n 36.93013456125331\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-04","publicationStatus":"PW","scienceBaseUri":"55cc6024e4b08400b1fe0fbc","contributors":{"authors":[{"text":"Holzer, Thomas L. tholzer@usgs.gov","contributorId":2829,"corporation":false,"usgs":true,"family":"Holzer","given":"Thomas","email":"tholzer@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":548929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noce, Thomas E. tnoce@usgs.gov","contributorId":3174,"corporation":false,"usgs":true,"family":"Noce","given":"Thomas","email":"tnoce@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":548930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Michael J. mjbennett@usgs.gov","contributorId":2783,"corporation":false,"usgs":true,"family":"Bennett","given":"Michael","email":"mjbennett@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":548928,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155229,"text":"tm5A11 - 2015 - U.S. Geological Survey Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples","interactions":[],"lastModifiedDate":"2015-08-12T16:00:22","indexId":"tm5A11","displayToPublicDate":"2015-08-12T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-A11","title":"U.S. Geological Survey Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples","docAbstract":"

This report addresses the standard operating procedures used by the U.S. Geological Survey’s Noble Gas Laboratory in Denver, Colorado, U.S.A., for the measurement of dissolved gases (methane, nitrogen, oxygen, and carbon dioxide) and noble gas isotopes (helium-3, helium-4, neon-20, neon-21, neon-22, argon-36, argon-38, argon-40, kryton-84, krypton-86, xenon-103, and xenon-132) dissolved in water. A synopsis of the instrumentation used, procedures followed, calibration practices, standards used, and a quality assurance and quality control program is presented. The report outlines the day-to-day operation of the Residual Gas Analyzer Model 200, Mass Analyzer Products Model 215–50, and ultralow vacuum extraction line along with the sample handling procedures, noble gas extraction and purification, instrument measurement procedures, instrumental data acquisition, and calculations for the conversion of raw data from the mass spectrometer into noble gas concentrations per unit mass of water analyzed. Techniques for the preparation of artificial dissolved gas standards are detailed and coupled to a quality assurance and quality control program to present the accuracy of the procedures used in the laboratory.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Water analysis in Book 5 Laboratory Analysis","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5A11","usgsCitation":"Hunt, A.G., 2015, Noble Gas Laboratory’s standard operating procedures for the measurement of dissolved gas in water samples: U.S. Geological Survey Techniques and Methods, book 5, chap. A11, 22 p., https://dx.doi.org/10.3133/tm5A11.","productDescription":"vi, 21 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065997","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":306599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/a11/coverthb.jpg"},{"id":306600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/a11/tm5a11.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 5-A11"}],"publicComments":"This report is Chapter 11 of Section A: Water analysis in Book 5 Laboratory Analysis.","contact":"

Director, Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
http://crustal.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2015-08-12","noUsgsAuthors":false,"publicationDate":"2015-08-12","publicationStatus":"PW","scienceBaseUri":"57f7eed3e4b0bc0bec09ed03","contributors":{"authors":[{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":565502,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70148618,"text":"70148618 - 2015 - Distribution and movements of Alaska-breeding Steller's Eiders in the nonbreeding period","interactions":[],"lastModifiedDate":"2015-08-12T11:01:20","indexId":"70148618","displayToPublicDate":"2015-08-12T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Distribution and movements of Alaska-breeding Steller's Eiders in the nonbreeding period","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-14-165.1","usgsCitation":"Martin, P.D., Douglas, D.C., Obritschkewitsch, T., and Torrence, S., 2015, Distribution and movements of Alaska-breeding Steller's Eiders in the nonbreeding period: The Condor, v. 117, no. 3, p. 341-353, https://doi.org/10.1650/CONDOR-14-165.1.","productDescription":"13 p.","startPage":"341","endPage":"353","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060383","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":471883,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-14-165.1","text":"Publisher Index Page"},{"id":438688,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ECETBG","text":"USGS data release","linkHelpText":"U.S. Fish and Wildlife Service Tracking Data for Steller's Eiders (Polysticta stelleri)"},{"id":306606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Barrow","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -180.52734375,\n 51.781435604431195\n ],\n [\n -180.52734375,\n 71.91088787611528\n ],\n [\n -155.56640625,\n 71.91088787611528\n ],\n [\n -155.56640625,\n 51.781435604431195\n ],\n [\n -180.52734375,\n 51.781435604431195\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55cc601fe4b08400b1fe0fb4","contributors":{"authors":[{"text":"Martin, Philip D.","contributorId":146442,"corporation":false,"usgs":false,"family":"Martin","given":"Philip","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":567895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":548909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Obritschkewitsch, Tim","contributorId":146443,"corporation":false,"usgs":false,"family":"Obritschkewitsch","given":"Tim","email":"","affiliations":[],"preferred":false,"id":567896,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torrence, Shannon","contributorId":71809,"corporation":false,"usgs":true,"family":"Torrence","given":"Shannon","email":"","affiliations":[],"preferred":false,"id":567897,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70155815,"text":"sir20155093 - 2015 - Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska","interactions":[],"lastModifiedDate":"2015-08-12T15:22:47","indexId":"sir20155093","displayToPublicDate":"2015-08-12T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5093","title":"Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska","docAbstract":"

The North Platte Natural Resources District (NPNRD) has been actively collecting data and studying groundwater resources because of concerns about the future availability of the highly inter-connected surface-water and groundwater resources. This report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, describes a groundwater-flow model of the North Platte River valley from Bridgeport, Nebraska, extending west to 6 miles into Wyoming. The model was built to improve the understanding of the interaction of surface-water and groundwater resources, and as an optimization tool, the model is able to analyze the effects of water-management options on the simulated stream base flow of the North Platte River. The groundwater system and related sources and sinks of water were simulated using a newton formulation of the U.S. Geological Survey modular three-dimensional groundwater model, referred to as MODFLOW–NWT, which provided an improved ability to solve nonlinear unconfined aquifer simulations with wetting and drying of cells. Using previously published aquifer-base-altitude contours in conjunction with newer test-hole and geophysical data, a new base-of-aquifer altitude map was generated because of the strong effect of the aquifer-base topography on groundwater-flow direction and magnitude. The largest inflow to groundwater is recharge originating from water leaking from canals, which is much larger than recharge originating from infiltration of precipitation. The largest component of groundwater discharge from the study area is to the North Platte River and its tributaries, with smaller amounts of discharge to evapotranspiration and groundwater withdrawals for irrigation. Recharge from infiltration of precipitation was estimated with a daily soil-water-balance model. Annual recharge from canal seepage was estimated using available records from the Bureau of Reclamation and then modified with canal-seepage potentials estimated using geophysical data. Groundwater withdrawals were estimated using land-cover data, precipitation data, and published crop water-use data. For fields irrigated with surface water and groundwater, surface-water deliveries were subtracted from the estimated net irrigation requirement, and groundwater withdrawal was assumed to be equal to any demand unmet by surface water.

\n

The groundwater-flow model was calibrated to measured groundwater levels and stream base flows estimated using the base-flow index method. The model was calibrated through automated adjustments using statistical techniques through parameter estimation using the parameter estimation suite of software (PEST). PEST was used to adjust 273 parameters, grouped as hydraulic conductivity of the aquifer, spatial multipliers to recharge, temporal multipliers to recharge, and two specific recharge parameters. Base flow of the North Platte River at Bridgeport, Nebraska, streamgage near the eastern, downstream end of the model was one of the primary calibration targets. Simulated base flow reasonably matched estimated base flow for this streamgage during 1950–2008, with an average difference of 15 percent. Overall, 1950–2008 simulated base flow followed the trend of the estimated base flow reasonably well, in cases with generally increasing or decreasing base flow from the start of the simulation to the end. Simulated base flow also matched estimated base flow reasonably well for most of the North Platte River tributaries with estimated base flow. Average simulated groundwater budgets during 1989–2008 were nearly three times larger for irrigation seasons than for non-irrigation seasons.

\n

The calibrated groundwater-flow model was used with the Groundwater-Management Process for the 2005 version of the U.S. Geological Survey modular three-dimensional groundwater model, MODFLOW–2005, to provide a tool for the NPNRD to better understand how water-management decisions could affect stream base flows of the North Platte River at Bridgeport, Nebr., streamgage in a future period from 2008 to 2019 under varying climatic conditions. The simulation-optimization model was constructed to analyze the maximum increase in simulated stream base flow that could be obtained with the minimum amount of reductions in groundwater withdrawals for irrigation. A second analysis extended the first to analyze the simulated base-flow benefit of groundwater withdrawals along with application of intentional recharge, that is, water from canals being released into rangeland areas with sandy soils. With optimized groundwater withdrawals and intentional recharge, the maximum simulated stream base flow was 15–23 cubic feet per second (ft3/s) greater than with no management at all, or 10–15 ft3/s larger than with managed groundwater withdrawals only. These results indicate not only the amount that simulated stream base flow can be increased by these management options, but also the locations where the management options provide the most or least benefit to the simulated stream base flow. For the analyses in this report, simulated base flow was best optimized by reductions in groundwater withdrawals north of the North Platte River and in the western half of the area. Intentional recharge sites selected by the optimization had a complex distribution but were more likely to be closer to the North Platte River or its tributaries. Future users of the simulation-optimization model will be able to modify the input files as to type, location, and timing of constraints, decision variables of groundwater withdrawals by zone, and other variables to explore other feasible management scenarios that may yield different increases in simulated future base flow of the North Platte River.

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Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, Nebraska 68512
http://ne.water.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2015-08-12","noUsgsAuthors":false,"publicationDate":"2015-08-12","publicationStatus":"PW","scienceBaseUri":"57f7eed3e4b0bc0bec09ed05","contributors":{"authors":[{"text":"Peterson, Steven M. 0000-0002-9130-1284 speterson@usgs.gov","orcid":"https://orcid.org/0000-0002-9130-1284","contributorId":847,"corporation":false,"usgs":true,"family":"Peterson","given":"Steven","email":"speterson@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flynn, Amanda T. aflynn@usgs.gov","contributorId":4411,"corporation":false,"usgs":true,"family":"Flynn","given":"Amanda","email":"aflynn@usgs.gov","middleInitial":"T.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":false,"id":566457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vrabel, Joseph 0000-0002-8773-0764 jvrabel@usgs.gov","orcid":"https://orcid.org/0000-0002-8773-0764","contributorId":1577,"corporation":false,"usgs":true,"family":"Vrabel","given":"Joseph","email":"jvrabel@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryter, Derek W. 0000-0002-2488-626X dryter@usgs.gov","orcid":"https://orcid.org/0000-0002-2488-626X","contributorId":3395,"corporation":false,"usgs":true,"family":"Ryter","given":"Derek","email":"dryter@usgs.gov","middleInitial":"W.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":567877,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70155245,"text":"sir20155098 - 2015 - Streamflow gains and losses in the Colorado River in northwestern Burnet and southeastern San Saba Counties, Texas","interactions":[],"lastModifiedDate":"2016-08-05T11:51:21","indexId":"sir20155098","displayToPublicDate":"2015-08-12T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5098","title":"Streamflow gains and losses in the Colorado River in northwestern Burnet and southeastern San Saba Counties, Texas","docAbstract":"

In October 2012, the U.S. Geological Survey (USGS), in cooperation with the Central Texas Groundwater Conservation District, began an assessment to better understand if and where groundwater from the Ellenburger-San Saba aquifer is discharging to the Colorado River, and if and where Colorado River streamflow is recharging the Ellenburger-San Saba aquifer in the study area. Discharge measurements were made to determine if different reaches of the Colorado River in northwestern Burnet and southeastern San Saba Counties are gaining or losing streamflow, the locations and quantities of gains and losses, and whether the gains and losses can be attributed to interaction between the river and the Ellenbuger-San Saba aquifer. To assess streamflow gains and losses, two sets of synoptic gain-loss discharge measurements representing different streamflow conditions were completed. In the first gain-loss streamflow survey during December 3–6, 2012 (hereinafter the fall 2012 gain-loss survey), discharge measurements were made at low-flow conditions ranging from about 30 to 60 cubic feet per second (ft3/s) at seven locations along the Colorado River. In the second gain-loss streamflow survey during May 31–June 1, 2014 (hereinafter the spring 2014 gain-loss survey), discharge measurements were made at high-flow conditions ranging from about 660 to 900 ft3/s at 12 locations along the Colorado River.

\n

During the fall 2012 gain-loss survey, verifiable gains or losses of streamflow were identified in 4 of 6 reaches (the difference in measured discharge between the upstream and downstream boundaries of the reach was larger than the sum of potential errors associated with the two discharge measurements). The two reaches with a verifiable gain in streamflow cross areas where the Ellenburger-San Saba aquifer crops out. The more upstream of the two reaches with verifiable losses crosses a small part of the Ellenburger-San Saba aquifer outcrop and confining units (Point Peak Member and Morgan Creek Limestone); it is possible streamflow losses in this reach are in the form of recharge to the Ellenburger-San Saba aquifer; little streamflow is likely lost to the underlying formations in the downstream part of the reach, which consists of relatively impermeable aquifer confining units exposed at land surface. The more downstream of the two reaches where a verifiable loss of streamflow was measured also flows across relatively impermeable confining units before crossing the Mid-Cambrian aquifer outcrop in the lower part of the reach; most of the streamflow losses in this reach were likely a result of water infiltrating into the subsurface from the streambed and providing recharge to the relatively permeable Mid-Cambrian aquifer.

\n

During the spring 2014 gain-loss survey, 11 reaches were combined into 3 in an attempt to consolidate gains and losses as well as group reaches within the same hydrogeologic units. An unverifiable loss was measured in the reach farthest upstream, which crosses a combination of alluvium and Ellenburger-San Saba aquifer outcrop, whereas an unverifiable gain was measured in the middle reach, which crosses each of the different hydrogeologic units represented in the study area. The reach farthest downstream crosses an area where only the Ellenburger-San Saba aquifer crops out; a streamflow gain of 123 ft3/s was measured in this reach, exceeding the potential error of 93.9 ft3/s. The verifiable streamflow gain in this downstream reach implies the Ellenburger-San Saba aquifer was discharging groundwater to the Colorado River in this part of the study area under the hydrologic conditions of the spring 2014 gain-loss survey.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155098","collaboration":"Prepared in cooperation with the Central Texas Groundwater Conservation District","usgsCitation":"Braun, C.L., and Grzyb, S.D., 2015, Streamflow gains and losses in the Colorado River in northwestern Burnet and southeastern San Saba Counties, Texas, 2012–14: U.S. Geological Survey Scientific Investigations Report 2015–5098, 32 p., https://dx.doi.org/10.3133/sir20155098.","productDescription":"v, 32 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-062274","costCenters":[{"id":105,"text":"Alabama Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":306566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5098/coverthb.jpg"},{"id":306567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5098/sir20155098.pdf","text":"Report","size":"6.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5098"}],"country":"United States","state":"Texas","county":"Burnet County, San Saba County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.48075866699219,\n 30.918131046738022\n ],\n [\n -98.48075866699219,\n 31.0376384361344\n ],\n [\n -98.38085174560547,\n 31.0376384361344\n ],\n [\n -98.38085174560547,\n 30.918131046738022\n ],\n [\n -98.48075866699219,\n 30.918131046738022\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
http://tx.usgs.gov/

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2015-08-12","noUsgsAuthors":false,"publicationDate":"2015-08-12","publicationStatus":"PW","scienceBaseUri":"57a5b8dae4b0ebae89b78a56","contributors":{"authors":[{"text":"Braun, Christopher L. 0000-0002-5540-2854 clbraun@usgs.gov","orcid":"https://orcid.org/0000-0002-5540-2854","contributorId":925,"corporation":false,"usgs":true,"family":"Braun","given":"Christopher","email":"clbraun@usgs.gov","middleInitial":"L.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":565299,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grzyb, Scott D. sgrzyb@usgs.gov","contributorId":145787,"corporation":false,"usgs":true,"family":"Grzyb","given":"Scott","email":"sgrzyb@usgs.gov","middleInitial":"D.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":565300,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}